RT Journal Article
SR Electronic
T1 Nanobody nuclear imaging allows noninvasive quantification of LAG-3 expression by tumor-infiltrating leukocytes and predicts response of immune checkpoint blockade
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.120.258871
DO 10.2967/jnumed.120.258871
A1 Quentin Lecocq
A1 Robin Maximilian Awad
A1 Yannick De Vlaeminck
A1 Wout De Mey
A1 Thomas Ertveldt
A1 Cleo Goyvaerts
A1 Geert Raes
A1 Kris Thielemans
A1 Marleen Keyaerts
A1 Nick Devoogdt
A1 Karine Breckpot
YR 2021
UL http://jnm.snmjournals.org/content/early/2021/05/14/jnumed.120.258871.abstract
AB Recent advances in the field of immune-oncology led to the discovery of next-generation immune checkpoints (ICPs). Lymphocyte activation gene-3 (LAG-3), being the most widely studied amongst them, is being explored as a target for the treatment of cancer patients. Several antagonistic anti-LAG-3 antibodies are being developed and are prime candidates for clinical application. Furthermore, validated therapies targeting CTLA-4, PD-1 or PD-L1 showed that only subsets of patients respond. This finding highlights the need for better tools for patient selection and monitoring. The potential of molecular imaging to detect ICPs noninvasively in cancer is supported by several (pre)clinical studies. Here, we report on a nanobody to evaluate whole-body LAG-3 expression in various syngeneic mouse cancer models using nuclear imaging. The radiolabeled nanobody detected LAG-3 expression on tumor-infiltrating lymphocytes (TILs) as soon as 1 hour after injection in the MC38, MO4 and TC-1 cancer models. The nanobody tracer visualized a compensatory upregulation of LAG-3 on TILs in MC38 tumors of mice treated with PD-1 blocking antibodies. When PD-1 blockade was combined with LAG-3 blockade, a synergistic effect on tumor growth delay was observed. In conclusion, these findings consolidate LAG-3 as a next-generation ICP and support the use of nanobodies as tools to noninvasively monitor the dynamic evolution of LAG-3 expression by TILs. This could be exploited to predict therapy outcome.